An agent-based approach in homeostatic control systems: Thermoregulation

Emek, Sevcan; Evren, Vedat; Bora, Şebnem

doi:10.1109/cicn.2017.8319367

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…In our approach, the agent-based control system is composed of a set of dynamic number of autonomous agents. We defined three important agents: a receptor agent, a controller agent and an effector agent [7,13]. Figure 2 shows the negative feedback control mechanism that consists of interacting agents.…”

Section: Methodsmentioning

confidence: 99%

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Simulation of glucose regulating mechanism with an agent-based software engineering tool

Emek

Evren²,

Bora³

2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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This study provides a detailed explanation of a regulating mechanism of the blood glucose levels by an agent-based software engineering tool. Repast Simphony which is used in implementation of this study is an agent-based software engineering tool based on the object-oriented programming using Java language. Agent-based modeling and simulation is a computational methodology for simulating and exploring phenomena that includes a large set of active components represented by agents. The agents are main components situated in space and time of agent-based simulation environment. In this study, we present hormonal regulation of blood glucose levels by our improved agent-based control mechanism. Hormonal regulation of blood glucose levels is an important process to maintain homeostasis inside the human body. We offer a negative feedback control mechanism with agent-based modeling approach to regulate the secretion of insulin hormone which is responsible for increasing the blood glucose levels. The negative feedback control mechanism run by three main agents that interact with each other to perform their local actions in the simulation environment. The result of this study shows the local behavior of the agents in the negative feedback loop and illustrates how to balance the blood glucose levels. Finally, this study which is thought a potential implementation of agent-based modeling and simulation may contribute to the exploration of other homeostatic control systems inside the human body.

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Section: Methodsmentioning

confidence: 99%

“…Each agent publishes the messages to its listeners and subscribes to the corresponding agent. The listener agent receives the messages and regulates its behavior according to the current environment situation [7,13].…”

Section: Figure 2 Negative Feedback Control Mechanism Of Hormonal Rementioning

confidence: 99%

Simulation of glucose regulating mechanism with an agent-based software engineering tool

Emek

Evren²,

Bora³

2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, the queen is responsible for repopulating the brood. Simple rules are assigned to each agent to govern local behaviors and interactions, including motion, population dynamics, and solution analysis (refer to Table 1) (Reynolds 1987;Chatterjee and Chakrabartty 2018;Zhang et al 2018;Karaboga and Akay 2009;Emek et al 2017;Schmickl and Karsai 2018).…”

Section: Agent-based Model Of Bees and Flowersmentioning

confidence: 99%

Artificial Honeybee Colony Algorithm: An Adaptive Optimization Method for Generating Migrating Search SpacesOptimization Method for Generating Migrating Search Spaces

Nichols,

Mulani,

Phillips

2024

Preprint

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Leveraging the adaptive characteristics of biological superorganisms, the Artificial Honeybee Colony (AHC) algorithm is an agent-based algorithm that integrates pollination models, particle swarm dynamics, and mutualistic plant-pollinator relations to generate new solution spaces and search for and generate resilient (or optimal) solutions to complex or nonlinear problems. The search space or environment adapts as solutions propagate using a density clustering algorithm, where only the higher quality solutions survive to cultivate into increasingly denser clusters via a pollination model. For verification, the AHC's capabilities were tested against particle swarm optimization and gradient descent with golden sections search for five benchmark functions given three different initial guesses ranging in proximities to the optimal solution. The AHC outperformed the other two methods in all five tests, locating optimal solutions in every case regardless of the initial guess' proximity to the optimum. Additionally, a global sensitivity analysis determined the most sensitive tunable parameter to be the pollination cluster radius, which determines the area new solutions appear within at each iteration via pollination. The results of these tests and applications demonstrate how the AHC's adaptive characteristics are beneficial in optimizing the resilience of highly interconnected, nonlinear, or complex problems where the user may have little to no former knowledge or intuition.

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Quantifying Characterizations of CubeSat Swarms Based on Chaotic Circuit Analysis

Nichols

Baker

Hudack

2021

AIAA Scitech 2021 Forum

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An agent-based approach in homeostatic control systems: Thermoregulation

Cited by 5 publications

References 10 publications

Simulation of glucose regulating mechanism with an agent-based software engineering tool

Simulation of glucose regulating mechanism with an agent-based software engineering tool

Artificial Honeybee Colony Algorithm: An Adaptive Optimization Method for Generating Migrating Search SpacesOptimization Method for Generating Migrating Search Spaces

Quantifying Characterizations of CubeSat Swarms Based on Chaotic Circuit Analysis

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